Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI
Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI
复制标题
DOI：
10.1016/j.mri.2016.11.020
复制
发表时间：
2017-04-01
影响因子：
2.5
通讯作者：
Park, Sung-Hong
中科院分区：
文献类型：
--
作者：
Kim, Ki Hwan;Park, Sung-Hong
平衡稳态自由旋进（bSSFP）MR序列在临床中经常使用，但对非共振效应敏感，这可能会导致带状伪影。通常，多个bSSFP数据集以不同的相位循环（PC）角度采集，然后以特殊的方式组合用于条带伪影抑制。已经提出了许多组合数据集的策略来抑制条带伪影，但是它们的性能仍然存在局限性，特别是当相位循环的bSSFP数据集的数量很小时。本研究的目的是开发一种基于学习的模型，以联合收割机结合多个相位循环的bSSFP数据集，以更好地抑制条带伪影。多层感知器（MLP）是一种前馈人工神经网络，由三层输入层、隐层和输出层组成。通过在3T下从人脑和膝关节获取的输入bSSFP数据集训练MLP模型，分别针对两个和四个PC角度进行。通过8或12个相位循环数据集的最大强度投影（MIP）生成无条带bSSFP图像，并将其用作训练输出层的目标。训练的MLP模型被应用到另一个大脑和膝关节数据集获得不同的扫描参数，也多个相位循环的bSSFP功能MRI数据集获得的大鼠大脑在9.4T，与传统的MIP方法相比。模拟也进行了验证的MLP方法。仿真和人体实验都表明，MLP抑制带状伪影显着，上级优于MIP的带状伪影抑制和SNR效率。对于9.4T fMRI数据，MLP也表现出优于MIP的上级性能，MIP不用于训练模型，同时视觉上很好地保留了fMRI图。人工神经网络是一种很有前途的技术，结合多个相位循环bSSFP数据集的条带伪影抑制。(C)2016 Elsevier Inc. All rights reserved.
The balanced steady-state free precession (bSSFP) MR sequence is frequently used in clinics, but is sensitive to off-resonance effects, which can cause banding artifacts. Often multiple bSSFP datasets are acquired at different phase cycling (PC) angles and then combined in a special way for banding artifact suppression. Many strategies of combining the datasets have been suggested for banding artifact suppression, but there are still limitations in their performance, especially when the number of phase-cycled bSSFP datasets is small. The purpose of this study is to develop a learning-based model to combine the multiple phase-cycled bSSFP datasets for better banding artifact suppression. Multilayer perceptron (MLP) is a feedforward artificial neural network consisting of three layers of input, hidden, and output layers. MLP models were trained by input bSSFP datasets acquired from human brain and knee at 3T, which were separately performed for two and four PC angles. Banding-free bSSFP images were generated by maximum-intensity projection (MIP) of 8 or 12 phase-cycled datasets and were used as targets for training the output layer. The trained MLP models were applied to another brain and knee datasets acquired with different scan parameters and also to multiple phase-cycled bSSFP functional MRI datasets acquired on rat brain at 9.4T, in comparison with the conventional MIP method. Simulations were also performed to validate the MLP approach. Both the simulations and human experiments demonstrated that MLP suppressed banding artifacts significantly, superior to MIP in both banding artifact suppression and SNR efficiency. MLP demonstrated superior performance over MIP for the 9.4T fMRI data as well, which was not used for training the models, while visually preserving the fMRI maps very well. Artificial neural network is a promising technique for combining multiple phase-cycled bSSFP datasets for banding artifact suppression. (C) 2016 Elsevier Inc. All rights reserved.